HOUSEDIFF: A MAP-BASED BUILDING CHANGE DETECTION
FROM HIGH RESOLUTION SATELLITE IMAGERY
USING GEOMETRIC OPTIMIZATION METHOD
N. Ishimaru ^ *, K. Iwamura?, Y. Kagawa?, T. Hino"
* Hitachi, Ltd., 1-18-13, Soto-Kanda, Chiyoda-ku, Tokyo, Japan
— (nobuhiro.ishimaru.yu, kazuaki.iwamura.wx)@hitachi.com
? Hitachi Solutions, Ltd., 4-12-7, Higashishinagawa, Shinagawa-ku, Tokyo, Japan
— (yoshiaki.kagawa.yy, takashi.hino.ea)@hitachi-solutions.com
KEY WORDS: Change Detection, Updating, Building, Urban, High resolution, Quickbird
ABSTRACT:
This paper presents a novel structural image analysis method based on geometric optimization techniques towards automatic
building change detection. The aim of this method is to efficiently detect the changes of various buildings such as small houses and
houses with complex roof in an urban area from high resolution satellite imagery by comparing with spatial database (maps). The
previous research has indicated of the effectiveness of a map-based building change detection approach, and further investigation
suggests the following three problems; (1) the large diversity of building types, roof shape, roof materials, illumination condition
and shadow, (2) the difficulty of imagery and maps matching which normally leads to considerable position error, (3) the capacity
of extracting various types of newly-built buildings. To solve these problems, we propose a new geometric optimization method
which consists of the following two steps; (1) the building recognition based on a combinatorial optimization method for optimal
building boundary extraction, (2) the newly-built building extraction based on an optimal building hypothesis search method. The
experimental results showed that the detection rate was approximately 89% for existing and changed buildings, and approximately
83% for newly-built buildings. These results demonstrate the effectiveness of the proposed geometric optimization methods to
integrate bottom-up and top-down analysis. By combining the locally detected image features with consideration of regional
contexts from map, our method can achieve highly accurate building change detection in urban area. The method has been applied
to a building change detection service named "HouseDiff" and succeeded in assisting users.
1. INTRODUCTION buildings with dark flat roof to the complex buildings with
various types of roof shape, roof materials, illumination
Timely and precise update and maintenance of spatial database condition and shadow. Second is the difficulty of imagery and
has been a challenge for the practical GIS applications such as maps matching which normally leads to considerable position
land use management and disaster management. As shown in error especially in the case of using simplified map. Third is
advanced examples of QuickBird, WorldView-1, and the capacity of extracting various types of newly-built buildings.
WorldView-2 from DigitalGlobe, the recent development of ^ To solve these problems and improve the recognition accuracy,
high resolution satellite in image accuracy and coverage, and we propose a new geometric optimization method which
short observation repetition makes it an ideal data source for consists of the following two steps. The first step is the
the purpose of automatic building change detection. building recognition based on a combinatorial optimization
method for optimal building boundary extraction so as to solve
This paper presents a novel structural image analysis method the first and second problems. The second step is the newly-
based on geometric optimization techniques towards automatic
building change detection. The aim of this method is to
efficiently detect the changes of various buildings such as
small houses and houses with complex roof in an urban area
from high resolution satellite imagery by comparing with
spatial database (maps). Several different approaches have
been proposed for building change detection as well as map
feature extraction, such as feature-based (Kazama et al, 2010;
Guo et al, 2010), rule-based (Ishimaru et al, 2005), model-
based (Fisher et al, 1998; Huertas et al, 1998), and map-based
(Ogawa et al, 1999). The previous research, map-based
approach, has indicated of the effectiveness of top-down
(model-driven) image analysis approach using map polygons as
building models. The simulated high resolution satellite
imagery was applied in the research, and our further
investigation using the actual high resolution satellite imagery
suggests the following three problems. First is the large
diversity of building types which range from the simple
built building extraction based on an optimal building
hypothesis search method to solve the first and third problems.
Figure 1 shows an overall framework of a map-based building
change detection algorithm.
Extended from
the pre
previous method
ding recogni Inask image building extraction
Sauer Existing //Newly-built
(emolishe 2 building building
need to re-examine)
Map database Building change detection database
Figure 1. Framework of a map-based building change
detection algorithm
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